基于专利信息分析的中国3D生物打印技术发展与产业化研究*

目的:基于专利信息对我国3D生物打印技术的发展态势进行分析。方法:本文基于incopat和TDA两大专利分析平台对中国3D生物打印的专利发展态势从专利统计分析与专利计量分析两个维度进行了跨库组合分析,总结了我国3D生物打印技术的专利前沿动态特征。结果:研究发现,中国3D生物打印技术从2013年起进入专利激增态势,中国作为潜在技术市场的国际竞争日趋激烈,本文还从专利申请人、技术领域分布、专利文本关键词聚类、专利价值、专利合作等方面进行了深度挖掘分析。结论:最后,结合对中国3D生物打印专利申请人的专利产业化案例深度分析与专利特征总结,为中国3D生物打印技术发展与产业化提供参考建议。     

Determination of the Blood,Hormone and Obesity Value Ranges that Indicate the Breast Cancer,Using Data Mining Based Expert System

Breast cancer is a dangerous type of cancer that spreads into other organs over time. Therefore, medical studies are being done for the early diagnosis by means of the anthropometric data and blood analysis values besides the mammographic and histological findings. However, medical studies have identified only cancer-related values but the value ranges indicating the cancer have not been determined yet. Concurrently the automated diagnostic systems are being developed to assist medical specialists in biomedical engineering studies. The range of values or boundaries indicating the cancer are automatically determined in biomedical methods, but only the diagnostic result is presented. Because of this, biomedical studies don't provide enough opportunity for medical experts to evaluate the relationship between values and result. In this study, decision trees that is one of data mining method was applied to anthropometric data and blood analysis values to complete the mentioned deficiencies in breast cancer diagnosis aiming studies. The determined value ranges were also presented visually to medical experts understand them easily. The proposed diagnostic system has accuracy rate up to 90.52% and provides value ranges indicating the breast cancer as well as mathematically presents the relations between the values and cancer.     

Sequence analysis and rule development of predicting protein stability change upon mutation using decision tree model

Understanding the mechanism of the protein stability change is one of the most challenging tasks. Recently, the prediction of protein stability change affected by single point mutations has become an interesting topic in molecular biology. However, it is desirable to further acquire knowledge from large databases to provide new insights into the nature of them. This paper presents an interpretable prediction tree method (named iPTREE-2) that can accurately predict changes of protein stability upon mutations from sequence based information and analyze sequence characteristics from the viewpoint of composition and order. Therefore, iPTREE-2 based on a regression tree algorithm exhibits the ability of finding important factors and developing rules for the purpose of data mining. On a dataset of 1859 different single point mutations from thermodynamic database, ProTherm, iPTREE-2 yields a correlation coefficient of 0.70 between predicted and experimental values. In the task of data mining, detailed analysis of sequences reveals the possibility of the compositional specificity of residues in different ranges of stability change and implies the existence of certain patterns. As building rules, we found that the mutation residues in wild type and in mutant protein play an important role. The present study demonstrates that iPTREE-2 can serve the purpose of predicting protein stability change, especially when one requires more understandable knowledge.     

数据科学在旧石器考古学中的应用

从考古学诞生之初,对抽象数据的解读与分析就一直伴随。对于旧石器考古学而言,“人工制品”成为了传达史前物质文化信息的主要载体,对人工制品中所提取的数据进行科学解读,成为了复原古代人类历史的关键步骤。数据科学在旧石器考古学中的应用具有三个主要因素,分别是数理统计学、计算机应用,以及旧石器考古学的基础数据与核心科学问题以及理论知识,即采用某种或多种逻辑将旧石器考古学领域的数据进行基于计算机平台的数理统计,并借助计算机语言对庞大的数据进行快速计算,从而帮助我们解释和重建史前人类社会。在目前的旧石器考古学领域,研究者们已经不再满足于对标本所进行的基础的描述性信息统计,对数据进行科学的处理并系统解读的诉求前所未有的强烈,这种诉求不断推动着学科的发展,深化了我们原本对史前社会的认识,甚至开拓出了新的研究领域,极大地推动了旧石器考古学的发展。本文就数据科学的概念、技术路线,以及在旧石器考古学中的应用历史与发展前景做详细介绍,希望通过系统性的梳理,使更多读者熟悉相关的研究手段与具体技术,使更多考古学者对数据科学的应用产生兴趣,从而应用于相关的项目研究中。     

Predicting the subcellular localization of human proteins using machine learning and exploratory data analysis

Identifying the subcellular localization of proteins is particularly helpful in the functional annotation of gene products. In this study, we use Machine Learning and Exploratory Data Analysis （EDA） techniques to examine and characterize amino acid sequences of human proteins localized in nine cellular compartments. A dataset of 3,749 protein sequences representing human proteins was extracted from the SWISS-PROT database. Feature vectors were created to capture specific amino acid sequence characteristics. Relative to a Support Vector Machine, a Multi-layer Perceptron, and a Naive Bayes classifier, the C4.5 Decision Tree algorithm was the most consistent performer across all nine compartments in reliably predicting the subcellular localization of proteins based on their amino acid sequences （average Precision=0.88; average Sensitivity=0.86）. Furthermore, EDA graphics characterized essential features of proteins in each compartment. As examples, proteins localized to the plasma membrane had higher proportions of hydrophobic amino acids; cytoplasmic proteins had higher proportions of neutral amino acids; and mitochondrial proteins had higher proportions of neutral amino acids and lower proportions of polar amino acids. These data showed that the C4.5 classifier and EDA tools can be effective for characterizing and predicting the subcellular localization of human proteins based on their amino acid sequences.     

Putting background information about relative risks into conjugate prior distributions

In Bayesian and empirical Bayes analyses of epidemiologic data, the most easily implemented prior specifications use a multivariate normal distribution for the log relative risks or a conjugate distribution for the discrete response vector. This article describes problems in translating background information about relative risks into conjugate priors and a solution. Traditionally, conjugate priors have been specified through flattening constants, an approach that leads to conflicts with the true prior covariance structure for the log relative risks. One can, however, derive a conjugate prior consistent with that structure by using a data-augmentation approximation to the true log relative-risk prior, although a rescaling step is needed to ensure the accuracy of the approximation. These points are illustrated with a logistic regression analysis of neonatal-death risk.     

Statistical inference for serial dilution assay data

Serial dilution assays are widely employed for estimating substance concentrations and minimum inhibitory concentrations. The Poisson-Bernoulli model for such assays is appropriate for count data but not for continuous measurements that are encountered in applications involving substance concentrations. This paper presents practical inference methods based on a log-normal model and illustrates these methods using a case application involving bacterial toxins.     

Horned lizard (Phrynosoma) phylogeny inferred from mitochondrial genes and morphological characters: understanding conflicts using multiple approaches

The genus Phrynosoma includes 13 species of North American lizards characterized by unique and highly derived morphologies and ecologies. Understanding interspecific relationships within this genus is essential for testing hypotheses about character evolution in this group. We analyzed mitochondrial ND4 and cytochrome b gene sequence data from all species of Phrynosoma in conjunction with a previously published dataset including 12S and 16S rRNA gene sequences and morphological characters. We used multiple phylogenetic methods and diagnostic tests for data combinability and taxonomic congruence to investigate the data in separate and combined analyses. Separate data partitions resulted in several well-supported lineages, but taxonomic congruence was lacking between topologies from separate and combined analyses. Partitioned Bremer support analyses also reveals conflict between data partitions in certain tree regions. When taxa associated with well-supported clades were removed from analyses, phylogenetic signal was lost. Combined, our results initially suggest conflict between data partitions, but further tests show the data are only appropriate for phylogenetic reconstruction of those parts of the topology that were well resolved. Nonetheless, our data analyses reveal five well-supported clades: (1) Phrynosoma ditmarsi and Phrynosoma hernandesi, (2) P. ditmarsi, P. hernandesi, and Phrynosoma douglasii, (3) P. ditmarsi, P. hernandesi, P. douglasii, and Phrynosoma orbiculare, (4) Phrynosoma mcallii and Phrynosoma platyrhinos, and (5) Phrynosoma braconnieri and Phrynosoma taurus.     

Integration and cross-validation of high-throughput gene expression data: comparing heterogeneous data sets

Data analysis--not data production--is becoming the bottleneck in gene expression research. Data integration is necessary to cope with an ever increasing amount of data, to cross-validate noisy data sets, and to gain broad interdisciplinary views of large biological data sets. New Internet resources may help researchers to combine data sets across different gene expression platforms. However, noise and disparities in experimental protocols strongly limit data integration. A detailed review of four selected studies reveals how some of these limitations may be circumvented and illustrates what can be achieved through data integration.